1. Field of the Invention
The present invention relates to a controller for controlling a system, having a plurality of candidate propositions or functions which result in a response, with the intention of optimising an objective function of the system. In particular, the present invention relates to controllers for systems presenting marketing propositions on the Internet, but is not limited thereto.
2. Description of Related Art
The last ten years has seen the development and rapid expansion of a technology sector known as Customer Relationship Management (CRM). This technology relates to hardware, software, and business practices designed to facilitate all aspects of the acquisition, servicing and retention of customers by a business.
One aspect of this technology involves using and applying business intelligence to develop software solutions for automating some of the processes involved in managing customer relationships. The resultant software solution can be applied wherever there is a vendor and a purchaser, i.e. to both business-to-private consumer relationships, and business-to-business relationships. Moreover, these solutions can be deployed in particular configurations to support CRM activities in different types of customer channel. For example, CRM technology can be used to control and manage the interactions with customers through telephone call-centres (inbound and outbound), Internet web sites, electronic kiosks, email and direct mail.
One of the principal functions of a CRM software solution is to maximize the efficiency of exchanges with customers. The first requirement for maximizing the efficiency of any particular business interface is to define a specific efficiency metric, success metric, or objective function, which is to be optimized. Typically this objective function relates to the monetary gains achieved by the interface, but is not limited thereto. It could for example relate to the minimization of customer attrition from the entry page of a web-site, or the maximisation of policy renewals for an insurance company using call centre support activities. In addition, the metric could be a binary response/non-response measurement or some other ordinal measure. The term objective function will be employed herein to encompass all such metrics.
For the sake of clarity only, the remainder of this specification will be based on systems which are designed to maximize either the number of purchase responses or the monetary responses from customers.
As an example, a web site retails fifty different products. There are therefore a plurality of different candidate propositions that are available for presentation to the visiting customer, the content of those propositions can be predetermined and the selection of the proposition to be presented is controlled according to a campaign controller. The candidate proposition is in effect a marketing proposition for the product in question.
When a customer visits the web site, an interaction event occurs in that a candidate proposition (marketing proposition) is presented to the customer (for example by display) according to the particular interaction scenario occurring between the customer and the web site and proposition. The response behaviour of the customer to the marketing proposition, and hence the response performance of the proposition, will vary according to a variety of factors.
FIG. 1 illustrates the principal data vectors that may influence the response behaviour of a customer to a particular candidate proposition or marketing proposition during an interaction event. In each case, examples of the field types that might characterise the vector are given.
A Product/Service Data Vector may contain fields which describe characteristics of the product which is the subject of the marketing proposition, such as size, colour, class, and a unique product reference number, although others may clearly be employed.
A Positioning Data Vector may contain information about the way in which the marketing proposition was delivered, for example, the message target age group, price point used and so on.
A Customer Data Vector may contain a number of explicit data fields which have been captured directly from the customer, such as the method of payment, address, gender and a number of summarized or composite fields which are thought to discriminate this customer from others. The vector may also contain data fields which represent inferred characteristics based upon previously observed behaviour of the customer. The summarized or composite fields can include fields such as the total value of purchases to date, the frequency of visits of the customer, and the date of last visit. Collectively this Customer Data Vector is sometimes known as a customer profile.
An Environment Vector may contain descriptors of the context of the marketing proposition, for example, the marketing channel used, the time of day, the subject context in which the proposition was placed, although others may be used.
The objective of the campaign controller is to select the candidate proposition to be presented which is predicted to optimise the objective function that can occur during the interaction event, that is to say produce a response performance or response value which produces the most success according to the selected metric, typically maximising the monetary response from the customer. This is the optimal solution. If one knew everything that could ever be known, then this optimal solution would be provided by the true best candidate proposition. In reality, the objective can be met to a degree by evaluating what the most likely next purchase may be for each customer visiting to the site, based on everything that they have done up to the present moment.
For the campaign controller to have the opportunity of exploiting relationships observed in historical interactions, data which characterizes the interaction event must be logged for each customer interaction. Each interaction event produces an interaction record containing a set of independent variable descriptors of the interaction event plus the response value which was stimulated by the marketing proposition presented. After a number of customers have visited the web site, a data set of such interaction records is produced and it then becomes possible to identify the relationships between specific conditions of the interaction event and the probability of a specific response value or outcome.
The identification and mapping of these significant relationships, as shown in FIG. 2, is sometimes performed within a mathematical or statistical framework (Data Mining, Mathematical Modelling, Statistical Modelling, Regression Modelling, Decision Tree Modelling and Neural Network Training are terms that are applied to this type of activity). Sometimes no explicit mapping takes place, instead the data records are arranged in a special format (usually a matrix) and are stored as exemplar “cases” (terms used to describe this approach are often Collaborative Filtering, Case Based Reasoning and Value Difference Metric, though there are many other names give to specific variants of this approach) Clustering is a method that could also be placed in this group as it is a method of storing aggregations of exemplars. These exemplar cases are then used as references for future expected outcomes.
The general purpose of all approaches is to use observations of previous interaction events to discriminate the likely outcome of new interaction events such that marketing propositions with a high expected outcome of success can be preferentially presented to customers. Over a period of time, the consistent preferential presenting of marketing propositions with higher expectation response values delivers a cumulative commercial benefit.
The choice of the modelling method typically depends on such things as:    The number of different types of response values that need to be modelled;    The computer processing time available for building the model;    The computer processing time available for making predictions based upon the model;    The importance of robustness versus accuracy;    The need for temporal stability in an on-line application;    The simplicity of adaptation of the method for the problem at hand.
The two general approaches of learning from historical observations of interaction events are described briefly below with their principal strengths and weaknesses: